Forward-, reverse- and mixed- mode automatic differentiation combinators with a common API.

Type-level "branding" is used to both prevent the end user from confusing infinitesimals
and to limit unsafe access to the implementation details of each Mode.

Each mode has a separate module full of combinators.

Numeric.AD.Mode.Forward provides basic forward-mode AD. It is good for computing simple derivatives.

Numeric.AD.Mode.Reverse uses benign side-effects to compute reverse-mode AD. It is good for computing gradients in one pass. It generates a Wengert list (linear tape) using Data.Reflection.

Numeric.AD.Mode.Kahn uses benign side-effects to compute reverse-mode AD. It is good for computing gradients in one pass. It generates a tree-like tape that needs to be topologically sorted in the end.

Numeric.AD.Mode.Sparse computes a sparse forward-mode AD tower. It is good for higher derivatives or large numbers of outputs.

Maintainer's Corner

Readme for ad-4.3.2

ad

A package that provides an intuitive API for Automatic Differentiation (AD) in Haskell. Automatic differentiation provides a means to calculate the derivatives of a function while evaluating it. Unlike numerical methods based on running the program with multiple inputs or symbolic approaches, automatic differentiation typically only decreases performance by a small multiplier.

AD employs the fact that any program y = F(x) that computes one or more value does so by composing multiple primitive operations. If the (partial) derivatives of each of those operations is known, then they can be composed to derive the answer for the derivative of the entire program at a point.

This library contains at its core a single implementation that describes how to compute the partial derivatives of a wide array of primitive operations. It then exposes an API that enables a user to safely combine them using standard higher-order functions, just as you would with any other Haskell numerical type.

There are several ways to compose these individual Jacobian matrices. We hide the choice used by the API behind an explicit "Mode" type-class and universal quantification. This prevents users from confusing infinitesimals. If you want to risk infinitesimal confusion in order to get greater flexibility in how you curry, flip and generally combine the differential operators, then the Rank1.* modules are probably your cup of tea.

Features

Provides forward- and reverse- mode AD combinators with a common API.

Optional type-level "branding" is available to prevent the end user from confusing infinitesimals

Each mode has a separate module full of combinators, with a consistent look and feel.

Examples

You can compute derivatives of functions

Prelude Numeric.AD> diff sin 0 {- cos 0 -}
1.0

Or both the answer and the derivative of a function:

Prelude Numeric.AD> diff' (exp . log) 2
(2.0,1.0)

You can compute the derivative of a function with a constant parameter using auto:

or if your function takes multiple inputs, you can use grads, which returns an 'f-branching stream' of derivatives, that you can
inspect lazily. Somewhat more intuitive answers can be obtained by converting the stream into the polymorphically recursive
Jet data type. With that we can look at a single "layer" of the answer at a time:

Numeric.AD.Newton provides a number of combinators for root finding using Newton's method with quadratic convergence.

Numeric.AD.Halley provides a number of combinators for root finding using Halley's method with cubic convergence.

Numeric.AD.Rank1.* provides combinators for AD that are strictly rank-1. This makes it easier to flip and contort them with higher order functions at the expense of type safety when it comes to infinitsimal confusion.

Combinators

While not every mode can provide all operations, the following basic operations are supported, modified as appropriate by the suffixes below:

grad computes the gradient (vector of partial derivatives at a given point) of a function.

jacobian computes the Jacobian matrix of a function at a point.

diff computes the derivative of a function at a point.

du computes a directional derivative of a function at a point.

hessian computes the Hessian matrix (matrix of second partial derivatives) of a function at a point.

Combinator Suffixes

The following suffixes alter the meanings of the functions above as follows:

' also return the answer

With lets the user supply a function to blend the input with the output

F is a version of the base function lifted to return a Traversable (or Functor) result

s means the function returns all higher derivatives in a list or f-branching Stream

T means the result is transposed with respect to the traditional formulation (usually to avoid paying for transposing back)

0 means that the resulting derivative list is padded with 0s at the end.

NoEq means that an infinite list of converging values is returned rather than truncating the list when they become constant

Contact Information

Contributions and bug reports are welcome!

Please feel free to contact me through github or on the #haskell IRC channel on irc.freenode.net.